The overall goal of this research is to develop and utilize novel data analysis and computational modeling techniques to determine how different areas of the human brain interact during information processing. Information processing in the brain involves extensive interactions between multiple, hierarchically organized areas. A central question is: how does the human cerebral cortex combine bottom-up and top-down processes? Previously, we have found that the direction of current dipoles detected by magnetoencephalography (MEG) and electroencephalography (EEG) can be utilized for determining the type of incoming input (feedforward vs. feedback) into a cortical area. We propose to carry forward the development of our approach by exploring methods to better understand information flow within the cerebral cortex during perceptual and cognitive processing. In Aim 1, we will use computational modeling to investigate the effect of the spatial pattern of synaptic inputs across the cortical laminae on the current dipole observable by MEG and EEG. Furthermore, we will examine the effect of sulcal and gyral folding on the accuracy of determining the physiological direction the MEG and EEG source currents. In Aim 2, we will investigate the large-scale flow of information within a network of cortical areas using a combination of the current dipole direction analysis and Granger causality measures. This research is anticipated to enable non-invasive inference of information flow in networks of cortical areas. It will provide a novel way to apply MEG/EEG recordings to studies of cognitive processing and interpret MEG/EEG in the context of large-scale integrative theories of the brain. It could lead to a better understanding of the neural mechanisms underlying cognitive functions, as well as to potential applications for revealing mechanisms of neural disorders.